An individualized protein-based prognostic model to stratify pediatric patients with papillary thyroid carcinoma

Zhihong Wang, He Wang, Yan Zhou, Lu Li, Mengge Lyu, Chunlong Wu, Tianen He, Lingling Tan, Yi Zhu, Tiannan Guo, Hongkun Wu (), Hao Zhang () and Yaoting Sun ()
Additional contact information
Zhihong Wang: The First Hospital of China Medical University
He Wang: Westlake University
Yan Zhou: Westlake University
Lu Li: Westlake University
Mengge Lyu: Westlake University
Chunlong Wu: Westlake Omics (Hangzhou) Biotechnology Co., Ltd.
Tianen He: Westlake University
Lingling Tan: Westlake Omics (Hangzhou) Biotechnology Co., Ltd.
Yi Zhu: Westlake University
Tiannan Guo: Westlake University
Hongkun Wu: Zhejiang University School of Medicine
Hao Zhang: The First Hospital of China Medical University
Yaoting Sun: Westlake University

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract Pediatric papillary thyroid carcinomas (PPTCs) exhibit high inter-tumor heterogeneity and currently lack widely adopted recurrence risk stratification criteria. Hence, we propose a machine learning-based objective method to individually predict their recurrence risk. We retrospectively collect and evaluate the clinical factors and proteomes of 83 pediatric benign (PB), 85 pediatric malignant (PM) and 66 adult malignant (AM) nodules, and quantify 10,426 proteins by mass spectrometry. We find 243 and 121 significantly dysregulated proteins from PM vs. PB and PM vs. AM, respectively. Function and pathway analyses show the enhanced activation of the inflammatory and immune system in PM patients compared with the others. Nineteen proteins are selected to predict recurrence using a machine learning model with an accuracy of 88.24%. Our study generates a protein-based personalized prognostic prediction model that can stratify PPTC patients into high- or low-recurrence risk groups, providing a reference for clinical decision-making and individualized treatment.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-47926-w Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-47926-w

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-47926-w

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().